def test_group_agents_wrapper(self):
MultiAgentCartPole = make_multi_agent("CartPole-v0")
grouped_ma_cartpole = GroupAgentsWrapper(
env=MultiAgentCartPole({"num_agents": 4}),
groups={"group1": [0, 1], "group2": [2, 3]},
)
obs = grouped_ma_cartpole.reset()
self.assertTrue(len(obs) == 2)
self.assertTrue("group1" in obs and "group2" in obs)
self.assertTrue(isinstance(obs["group1"], list) and len(obs["group1"]) == 2)
self.assertTrue(isinstance(obs["group2"], list) and len(obs["group2"]) == 2)
def with_agent_groups(
self,
groups: Dict[str, List[AgentID]],
obs_space: gym.Space = None,
act_space: gym.Space = None) -> "MultiAgentEnv":
"""Convenience method for grouping together agents in this env.
An agent group is a list of agent IDs that are mapped to a single
logical agent. All agents of the group must act at the same time in the
environment. The grouped agent exposes Tuple action and observation
spaces that are the concatenated action and obs spaces of the
individual agents.
The rewards of all the agents in a group are summed. The individual
agent rewards are available under the "individual_rewards" key of the
group info return.
Agent grouping is required to leverage algorithms such as Q-Mix.
Args:
groups: Mapping from group id to a list of the agent ids
of group members. If an agent id is not present in any group
value, it will be left ungrouped. The group id becomes a new agent ID
in the final environment.
obs_space: Optional observation space for the grouped
env. Must be a tuple space. If not provided, will infer this to be a
Tuple of n individual agents spaces (n=num agents in a group).
act_space: Optional action space for the grouped env.
Must be a tuple space. If not provided, will infer this to be a Tuple
of n individual agents spaces (n=num agents in a group).
Examples:
>>> from ray.rllib.env.multi_agent_env import MultiAgentEnv
>>> class MyMultiAgentEnv(MultiAgentEnv): # doctest: +SKIP
... # define your env here
... ... # doctest: +SKIP
>>> env = MyMultiAgentEnv(...) # doctest: +SKIP
>>> grouped_env = env.with_agent_groups(env, { # doctest: +SKIP
... "group1": ["agent1", "agent2", "agent3"], # doctest: +SKIP
... "group2": ["agent4", "agent5"], # doctest: +SKIP
... }) # doctest: +SKIP
"""
from ray.rllib.env.wrappers.group_agents_wrapper import \
GroupAgentsWrapper
return GroupAgentsWrapper(self, groups, obs_space, act_space)
def with_agent_groups(
self,
groups: Dict[str, List[AgentID]],
obs_space: gym.Space = None,
act_space: gym.Space = None) -> "MultiAgentEnv":
"""Convenience method for grouping together agents in this env.
An agent group is a list of agent ids that are mapped to a single
logical agent. All agents of the group must act at the same time in the
environment. The grouped agent exposes Tuple action and observation
spaces that are the concatenated action and obs spaces of the
individual agents.
The rewards of all the agents in a group are summed. The individual
agent rewards are available under the "individual_rewards" key of the
group info return.
Agent grouping is required to leverage algorithms such as Q-Mix.
This API is experimental.
Args:
groups (dict): Mapping from group id to a list of the agent ids
of group members. If an agent id is not present in any group
value, it will be left ungrouped.
obs_space (Space): Optional observation space for the grouped
env. Must be a tuple space.
act_space (Space): Optional action space for the grouped env.
Must be a tuple space.
Examples:
>>> env = YourMultiAgentEnv(...)
>>> grouped_env = env.with_agent_groups(env, {
... "group1": ["agent1", "agent2", "agent3"],
... "group2": ["agent4", "agent5"],
... })
"""
from ray.rllib.env.wrappers.group_agents_wrapper import \
GroupAgentsWrapper
return GroupAgentsWrapper(self, groups, obs_space, act_space)
